Skip to main content

A Fuzzy Logic Approach to Gas Path Diagnostics in Aero-engines

  • Chapter
Computational Intelligence in Fault Diagnosis

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

Engine-related costs contribute a large fraction of the direct operating costs (DOCs) of an aircraft, because the propulsion system requires a significant part of the overall maintenance effort. Thus, to ensure competitive advantage in the aeroengine market, health monitoring systems with gas path diagnostics capability are highly desirable.

In this chapter, an application of fuzzy logic technology to gas path diagnostics for aero-engines performance analysis is presented and the setup procedure for a modern civil turbofan is described, as an example. The objective is to estimate the changes in engine component performance due to the engine degradation over time from the knowledge of only a few measurable parameters, inevitably affected by noise. This is a novel process that achieves effective diagnosis by means of a rule-based pattern-recognition methodology founded on fuzzy algebra, developed to provide an alternative technology versus conventional estimation algorithms.

The inherent capability of fuzzy logic to deal with gas path diagnostics difficulties, thanks to the use of fuzzy set theory and its rule-based nature, is highlighted. First, the problem of noisy measurements is treated at a fuzzy-set level. Second, at the system level the definition of fuzzy rules is used to map input sets of measurements into output faulty classes of performance parameters in a constrained search space; this enables a problem reduction aimed at overcoming the fact that the analytical formulation is undetermined.

The process quantifies the performance parameters’ deteriorations through a nonlinear approach, even in the presence of noisy measurements that typically complicate the diagnostic assessment. The diagnostics model’s setup as well as its outcome can be attained in a relatively short time, making this technique suitable for on-board use. The accuracy of the technique relative to simulated turbofan data is tested and its advantages and limitations are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kandel A (1986) Fuzzy mathematical techniques with applications. Addison-Welsey, USA.

    MATH  Google Scholar 

  2. Kosko B (1997) Fuzzy engineering. Prentice Hall, New Jersey.

    MATH  Google Scholar 

  3. Marinai L, Ogaji S, Sampath S and Singh R (2003a) Engine Diagnostics-Fuzzy Logic Approach. In: Proceedings of the Seventh International Conference on Knowledge-Based Intelligent Information & Engineering Systems — KES’03, Oxford, 3–5 September.

    Google Scholar 

  4. Marinai L, Singh R and Curnock B (2003b) Fuzzy-logic-based diagnostic process for turbofan engines. In: Proceedings of ISABE 2003, XVI International Symposium on Air Breathing Engines, Cleveland, Ohio, 31 August–5 September.

    Google Scholar 

  5. Marinai L, Singh R, Curnock B and Probert D (2003c) Detection and prediction of the performance deterioration of a turbofan engine. In: Proceedings of the International Gas Turbine Congress 2003, Tokyo, 2–7 November.

    Google Scholar 

  6. Marinai L (2004) Gas path diagnostics and prognostics for aero-engines using fuzzy logic and time series analysis (PhD Thesis). School of Engineering, Cranfield University.

    Google Scholar 

  7. Marinai L, Probert D and Singh R (2004) Prospects for aero gas-turbine diagnostics: a review. Applied Energy.

    Google Scholar 

  8. Mathioudakis K and Sieverding CH (2003) Gas Turbine Condition Monitoring & Fault Diagnosis. In: Von Karman Institute Lecture Series 2003-01, Brussels, Belgium, 13–17 January.

    Google Scholar 

  9. Volponi A (2003) Extending gas-path analysis coverage for other fault conditions. In: Von Karman Institute Lecture Series 2003-01, Gas Turbine Condition Monitoring & Fault Diagnosis, Brussels, Belgium, 13–17 January.

    Google Scholar 

  10. Zadeh LA (1969) Toward a theory of fuzzy systems. NASA CR-1432, Washington, DC.

    Google Scholar 

  11. Fuzzy Logic Toolbox User’s Guide. The MathWorks Inc., Natick, MA.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag London Limited

About this chapter

Cite this chapter

Marinai, L., Singh, R. (2006). A Fuzzy Logic Approach to Gas Path Diagnostics in Aero-engines. In: Palade, V., Jain, L., Bocaniala, C.D. (eds) Computational Intelligence in Fault Diagnosis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-631-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-84628-631-5_2

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-343-7

  • Online ISBN: 978-1-84628-631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics